code: def generate_constraint(conditions, ctype=None, join=None, **kwds):
      """converts a constraint solver to a ``mystic.constraints`` function.
  
      Args:
          conditions (object): a constraint solver, or list of constraint solvers.
          ctype (object, default=None): a ``mystic.coupler`` type, or a list of
              ``mystic.coupler`` types of the same length as *conditions*.
          join (object, default=None): ``and_`` or ``or_`` from ``mystic.constraints``.
  
      Returns:
          a ``mystic.constaints`` function built from the given constraints
  
      Notes:
          If ``join=None``, then apply the given constraints sequentially.
          Otherwise, apply the constraints concurrently using a constraints coupler.
  
      Warning:
          This constraint generator doesn't check for conflicts in conditions, but
          simply applies conditions in the given order. This constraint generator
          assumes that a single variable has been isolated on the left-hand side
          of each constraints equation, thus all constraints are of the form
          ``x_i = f(x)``. This solver picks speed over robustness, and relies on
          the user to formulate the constraints so that they do not conflict.
  
      Examples:
          >>> constraints = '''
          ...     x0 = cos(x1) + 2.
          ...     x1 = x2*2.'''
          >>> solv = generate_solvers(constraints)
          >>> constraint = generate_constraint(solv)
          >>> constraint([1.0, 0.0, 1.0])
          [1.5838531634528576, 2.0, 1.0]
  
          Standard python math conventions are used. For example, if an ``int``
          is used in a constraint equation, one or more variable may be evaluate
          to an ``int`` -- this can affect solved values for the variables.
  
          >>> constraints = '''
          ...     x2 = x0/2.
          ...     x0 >= 0.'''
          >>> solv = generate_solvers(constraints, nvars=3)
          >>> print(solv[0].__doc__)
          'x[2] = x[0]/2.'
          >>> print(solv[1].__doc__)
          'x[0] = max(0., x[0])'
          >>> constraint = generate_constraint(solv)
          >>> constraint([1,2,3])
          [1, 2, 0.5]
          >>> constraint([-1,2,-3])
          [0.0, 2, 0.0]"""
      # allow for single condition, list of conditions, or nested list
      if not list_or_tuple_or_ndarray(conditions):
          conditions = list((conditions,))
      else:
          pass  # XXX: should be fine...
  
      # discover the nested structure of conditions and ctype
      nc = nt = 0
      if ctype is None or not list_or_tuple_or_ndarray(ctype):
          nt = -1
      else:
          while tuple(flatten(conditions, nc)) != tuple(
              flatten(conditions)
          ):
              nc += 1
          while tuple(flatten(ctype, nt)) != tuple(flatten(ctype)):
              nt += 1
  
      if join is None:
          pass  # don't use 'and/or' to join the conditions
      # elif nc >= 2: # join when is tuple of tuples of conditions
      else:  # always use join, if given (instead of only if nc >= 2)
          if (
              nt >= nc
          ):  # there as many or more nested ctypes than conditions
              p = iter(ctype)
              return join(
                  *(
                      generate_constraint(c, next(p), **kwds)
                      for c in conditions
                  )
              )
          return join(
              *(
                  generate_constraint(c, ctype, **kwds)
                  for c in conditions
              )
          )
      # flatten everything and produce the constraint
      conditions = list(flatten(conditions))
  
      # allow for single ctype, list of ctypes, or nested list
      if ctype is None:
          from mystic.coupler import inner  # XXX: outer ?
  
          ctype = list((inner,)) * len(conditions)
      elif not list_or_tuple_or_ndarray(ctype):
          ctype = list((ctype,)) * len(conditions)
      else:
          pass  # XXX: is already a list, should be the same len as conditions
      ctype = list(flatten(ctype))
  
      # iterate through solvers, building a compound constraints solver
      cf = lambda x: x
      cfdoc = ""
      for wrapper, condition in zip(ctype, conditions):
          cfdoc += "%s: %s\n" % (wrapper.__name__, condition.__doc__)
          apply = wrapper(condition, **kwds)
          cf = apply(cf)
      cf.__doc__ = cfdoc.rstrip("\n")
      cf.__name__ = "constraint"
      return cf
  
imports: from numpy import ndarray, asarray, any
  from mystic._symbolic import solve
  from mystic.tools import list_or_tuple_or_ndarray, flatten
label_desc: Use image node_id 26 for calling a global function with example usage: generate_constraint(conditions, ctype, join) and returns: cf, join, join